DOI

https://doi.org/10.1299/jsmemecj.2021.J063-03

Abstract

We report development of a new polymer electret CTX-A/APDEA based on Cyclic Transparent Optical Polymer (CYTOP, AGC Chemicals). With a 15-μm-thick film, the surface potential of the synthesized electret kept at -3 kV for more than 800 hours and the peak temperature under thermally stimulated discharge (TSD) measurement of it was 187 °C, which both outperformed all commercialized CYTOP polymer electrets. In our study, deep reinforcement learning is employed, where density functional theory (DFT) is used to characterize the material property and ChemTS is employed for exploring the unknown chemical space. Besides, functional group enrichment of the generated molecules is analyzed statistically for incorporating interpretable knowledge while solid-state quantum chemical analysis based on PCM-DFT is conducted for studying CTX-A/APDEA. Our results are the first to demonstrate the successful application of machine learning in polymer electret design and the combination of expert knowledge with artificial intelligence.